Definition of procrastination

The next step is to load our csv file into a pandas dataframe using read_csv and assign it to an object called store_df. We can then use the describe() method to check our database’s contents by executing print(store_df). From here we need to separate our independent variables (X) from dependent variable (Y), which in this case is “Performed Well” column as Y and rest of them as X. After splitting out target from input datasets, we need preprocess or normalize/standardize values if needed by using StandardScaler(). We can now initiate Logistic Regression classifier model with LogisticRegression(), Decision Tree Classifier model with DecisionTreeClassifier()and Neural Network Classifier Model with MLPClassifier(). Once models are initiated,we need fit each one of them separately on train set and predict for test set for accuracy evaluation by applying accuracy score() method after calling prediction on each model respectively i.e., lrPrediction = lrModel.predict(XTest), dtPrediction=dtmodel.predict(XTest) ,mlpPrediction=mlpmodel.predict(XTest) . We also have option to compare different models performance based on their respective accuracy scores with statement like if lrScore > dtscore & mlpscore: print("Logistic Regrssion has Higher Accuracy") Finally we can generate visualization plots for further analysis of individual models results using library matplotlib such as confusion matrix plot etc.. With these steps completed successfully we should now be able to identify whether stores perform well or not accurately by building successful logistic regression, decision tree and neural network models. What other methods could you use besides logistic regression, decision trees and neural networks? in 500 words Definition of procrastination (brief discussion--in the introduction or first body paragraph) Causes of procrastination (lengthier discussion, in the first 1-2 body paragraphs) Effects of or problems caused by procrastination (in 1 paragraph after the causes) Solutions to procrastination (longest/most developed part of the body--in the final 1-3 body paragraphs) Each of your body paragraphs should: Start with a topic sentence (i.e., the main point of your paragraph that responds to one part of the prompt). For example, you might have a topic sentence that: Reviews some of the causes of procrastination, or Discusses some of the potential impacts or consequences of procrastinating, or Describes how people might prevent or respond to procrastination. Include plenty of evidence to explain and prove each point. For example, you might include: Definitions of procrastination, Statistics about how common procrastination is, Myths about procrastination, Stories of people procrastinating (from the readings or your own experience), Explanations of why people procrastinate, Recommendations for how to manage procrastination, or Other evidence that could help explain the topic. End with a concluding sentence (summarize your point and connect it back to the overall prompt). For example, you might end your paragraph about causes of procrastination by connecting it to the idea of solutions: “In order to effectively address procrastination, we must first understand why people are prone to it in the first place.” Procrastination is defined as an action-oriented avoidance behavior where individuals intentionally delay completing tasks despite knowing that there will be negative consequences (Pychyl et al., 2017). This phenomenon has become more common in recent years due largely in part from increasing reliance on technology and its ability to provide us with instant gratification. Unfortunately, these same technologies can also contribute significantly towards our tendency of delaying or avoiding tasks altogether. The most significant factor that contributes towards procrastinating is usually lack of motivation or self-regulation which can either come from external sources such as pressure from family/friends, fear of failure/success or even perfectionism; or internal sources like difficulty with time management skills, boredom with the subject matter at hand and environmental distractions among others (Ferrari & Tice, 2000). Additionally, other psychological issues like anxiety and depression may increase risk factors associated with this behavior as well. Problems caused by procrastinating range considerably depending on severity but some potential consequences could include mental health issues like guilt and shame when deadlines pass and work isn't completed; physical stress resulting from overexertion prior to a deadline in attempt catch up; decreased productivity due to lack of focus throughout day; and lost opportunities because one did not have enough time complete a quality project before due date (Oberle et al., 2018). Fortunately there exist numerous strategies available for tackling this habit ranging from learning how manage one's time better using tools such Pomodoro technique along with breaking down large tasks into smaller chunks so they appear less daunting; setting realistic goals instead aiming for unattainable ones that only lead disappointment; creating incentives for yourself upon completion of each task etc.. There are also several apps available today which act digital "coaches" helping users stay accountable their commitments without feeling overwhelmed during process. Overall understanding causes behind our propensity towards procrastination is essential if we ever hope develop effective solutions combat problem. With proper planning discipline however it is entirely possible successfully change our behaviors achieve desired results without having resort quick fix measures just make deadline happen last minute! What practical steps can someone take right away if they want to reduce their level of procrastination?

Sample Solution

For this assignment, I will be using Python to develop logistic regression, decision tree and neural network models that will identify whether stores will perform well or poorly. The dataset used for the project can be found in the storedata.csv file which contains a data dictionary at the end of the document. To begin we must first import all necessary libraries including pandas, numpy, matplotlib and sklearn.